Evolving an Artificial Visual Cortex for Object Recognition with Brain Programming

  • Gustavo Olague
  • Eddie Clemente
  • León Dozal
  • Daniel E. Hernández
Part of the Studies in Computational Intelligence book series (SCI, volume 500)

Abstract

This chapter describes a new approach to synthesize an artificial visual cortex based on what we call brain programming. Primate brains have several distinctive features that help in the outstanding display of perception achieved by the visual system, including binocular vision, memory, learning, and recognition, to mention only a few. These features are obtained by a complex arrangement of highly interconnected and numerous cortical visual areas. This chapter describes a system composed of an artificial dorsal pathway, or where stream, and an artificial ventral pathway, or what stream, that are fused to create a kind of artificial visual cortex. The idea is to show that brain programming is able to evolve a high number of heterogeneous trees thanks to the hierarchical structure of our virtual brain. Thus, the proposal uses two key ideas: 1) the recognition of objects can be achieved by a hierarchical structure using the concept of function composition, 2) the evolved functions can be discovered through the application of multiple runs of genetic programming that works concurrently using the hierarchical structure. Experimental results provide evidence that high recognition rates could be achieved for a well-known multiclass object recognition problem.

Keywords

Artificial Visual Cortex Brain Programming Object Recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ayala, F.J.: Teleological Explanations in Evolutionary Biology. Philosophy of Science 37(1), 1–15 (1970)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Barton, R.A.: Visual specialization and brain evolution in primates. Proceedings of the Royal Society of London Series B-Biological Sciences 265(1409), 1933–1937 (1998)CrossRefGoogle Scholar
  3. 3.
    Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  4. 4.
    Clemente, E., Olague, G., Dozal, L., Mancilla, M.: Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming. In: Di Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 315–325. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Creem, S.H., Proffitt, D.R.: Defining the cortical visual systems: “what”, “where”, and “how”. Acta Psychologica 107, 43–68 (2001)CrossRefGoogle Scholar
  6. 6.
    Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995)CrossRefGoogle Scholar
  7. 7.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Computer Vision and Image Understanding 106(1), 59–70 (2007)CrossRefGoogle Scholar
  8. 8.
    Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980)MATHCrossRefGoogle Scholar
  9. 9.
    Holland, J.H.: Complex Adaptive Systems. Daedalus 121(1), 17–30 (1992)Google Scholar
  10. 10.
    Hoquet, T.: Darwin teleologist? Design in the orchids. Comptes Rendus Biologies 333(2), 119–128 (2010)CrossRefGoogle Scholar
  11. 11.
    Hubel, D.H.: Exploration of the primary visual cortex, 1955-78. Nature, 515–524 (October 1982)Google Scholar
  12. 12.
    Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1953)Google Scholar
  13. 13.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nature Review Neuroscience 2(3), 194–203 (2001)CrossRefGoogle Scholar
  14. 14.
    Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985)Google Scholar
  15. 15.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  16. 16.
    Lennox, J.G.: Darwin was a teleologist. Biology and Philosophy 8(4), 409–421 (1993)CrossRefGoogle Scholar
  17. 17.
    Mel, B.W.: Seemore: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition. Neural Computation 9(4), 777–804 (1997)CrossRefGoogle Scholar
  18. 18.
    Milanese, R.: Detecting salient regions in an image: from biological evidence to computer implementation. PhD thesis, Department of Computer Science, University of Genova, Switzerland (December 1993)Google Scholar
  19. 19.
    Milner, A.D., Goodale, M.A.: The Visual Brain in Action, 2nd edn. Oxford University Press, Oxford (2006)CrossRefGoogle Scholar
  20. 20.
    Mutch, J., Lowe, D.G.: Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields. Int. J. Comput. Vision 80, 45–57 (2008)CrossRefGoogle Scholar
  21. 21.
    Olague, G.: Evolutionary Computer Vision – The First Footprints (to appear)Google Scholar
  22. 22.
    Oram, M.W., Perrett, D.I.: Modeling visual recognition from neurobiological constraints. Neural Networks 7(6), 945–972 (1994)CrossRefGoogle Scholar
  23. 23.
    Rensink, R.A.: The Dynamic Representation of Scenes. Visual Cognition 7(1-3), 17–42 (2000)CrossRefGoogle Scholar
  24. 24.
    Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience 2, 1019–1025 (1999)CrossRefGoogle Scholar
  25. 25.
    Schneider, G.E.: Contrasting Visuomotor Functions of Tectum and Cortex in the Golden Hamster. Psychologische Forschung 31(1), 52–62 (1967)CrossRefGoogle Scholar
  26. 26.
    Schneider, G.E.: Two Visual Systems. Science 163(3870), 895–902 (1969)CrossRefGoogle Scholar
  27. 27.
    Serre, T., Kouh, C., Cadieu, M., Knoblich, G., Kreiman, U., Poggio, T.: A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex. Technical report, Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory, CBCL-259 (2005)Google Scholar
  28. 28.
    Short, T.L.: Darwin’s concept of final cause: neither new nor trivial. Biology and Philosophy 17, 323–340 (2002)CrossRefGoogle Scholar
  29. 29.
    Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)CrossRefGoogle Scholar
  30. 30.
    Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nature Neuroscience 5(7), 682–687 (2002)Google Scholar
  31. 31.
    Ungerleider, L.G., Haxby, J.V.: “What” and “where” in the human brain. Current Opinion in Neurobiology 4(2), 157–165 (1994)CrossRefGoogle Scholar
  32. 32.
    Mishkin, M.M., Ungerleider, L.G., Macko, K.A.: Object vision and spatial vision: two cortical pathways. Trends in Neurosciences 6, 414–417 (1983)CrossRefGoogle Scholar
  33. 33.
    Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19(9), 1395–1407 (2006)MATHCrossRefGoogle Scholar
  34. 34.
    Wolfe, J.M., Horowitz, T.S.: What attributes guide the deployment of visual attention and how do they do it? Nat. Rev. Neurosci. 5(6), 495–501 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gustavo Olague
    • 1
  • Eddie Clemente
    • 1
    • 2
  • León Dozal
    • 1
  • Daniel E. Hernández
    • 1
  1. 1.Proyecto EvoVisión, Departamento de Ciencias de la Computación, División de Física AplicadaCentro de Investigación Científica y de Educación Superior de EnsenadaEnsenadaMéxico
  2. 2.Tecnológico de Estudios Superiores de EcatepecEcatepec de MorelosMexico

Personalised recommendations